SoroushVahidi/scidocs huggingface dataset:
Visfer: Camera-based visual data transfer for cross-device visualization
(2019)

Going beyond the desktop to leverage novel devices—such as smartphones, tablets, or large displays—for visual sensemaking typically requires supporting extraneous operations for device discovery, interaction sharing, and view management. Such operations can be time-consuming and tedious, and distract the user from the actual analysis. Embodied interaction models in these multi-device environments can take advantage of the natural interaction and physicality afforded by multimodal devices and help effectively carry out these operations in visual sensemaking. In this paper, we present cross-device interaction models for visualization spaces, that are embodied in nature, by conducting a user study to elicit actions from participants that could trigger a portrayed effect of sharing visualizations (and therefore information) across devices. We then explore one common interaction style from this design elicitation called Visfer, a technique for effortlessly sharing visualizations across devices using the visual medium. More specifically, this technique involves taking pictures of visualizations, or rather the QR codes augmenting them, on a display using the built-in camera on a handheld device. Our contributions include a conceptual framework for cross-device interaction and the Visfer technique itself, as well as transformation guidelines to exploit the capabilities of each specific device and a web framework for encoding visualization components into animated QR codes, which capture multiple frames of QR codes to embed more information. Beyond this, we also present the results from a performance evaluation for the visual data transfer enabled by Visfer. We end the paper by presenting the application examples of our Visfer framework.
CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum
(2016)

Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.
 Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.
Resin composite--state of the art.
(2011)

OBJECTIVES
The objective is to review the current state of the art of dental composite materials.


METHODS
An outline of the most important aspects of dental composites was created, and a subsequent literature search for articles related to their formulation, properties and clinical considerations was conducted using PubMed followed by hand searching citations from relevant articles.


RESULTS
The current state of the art of dental composites includes a wide variety of materials with a broad range of mechanical properties, handling characteristics, and esthetic possibilities. This highly competitive market continues to evolve, with the major emphasis in the past being to produce materials with adequate strength, and high wear resistance and polishability retention. The more recent research and development efforts have addressed the issue of polymerization shrinkage and its accompanying stress, which may have a deleterious effect on the composite/tooth interfacial bond. Current efforts are focused on the delivery of materials with potentially therapeutic benefits and self-adhesive properties, the latter leading to truly simplified placement in the mouth.


SIGNIFICANCE
There is no one ideal material available to the clinician, but the commercial materials that comprise the current armamentarium are of high quality and when used appropriately, have proven to deliver excellent clinical outcomes of adequate longevity.
On-road driver eye movement tracking using head-mounted devices
(2002)

It is now evident from anecdotal evidence and preliminary research that distractions can hinder the task of operating a vehicle, and consequently reduce driver safety. However with increasing wireless connectivity and the portability of office devices, the vehicle of the future is visualized as an extension of the static work place - i.e. an office-on-the-move, with a phone, a fax machine and a computer all within the reach of the vehicle operator. For this research a Head mounted Eye-tracking Device (HED), is used for tracking the eye movements of a driver navigating a test route in an automobible while completing various driving tasks. Issues arising from data collection of eye movements during the completion of various driving tasks as well as the analysis of this data are discussed. Methods for collecting video and scan-path data, as well as difficulties and limitations are also reported.
State-space model with deep learning for functional dynamics estimation in resting-state fMRI
(2016)

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.
LEARNING PHYSICAL DYNAMICS
(2017)

We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE’s compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.
An Ontology-based Context Model in Intelligent Environments
(2003)

Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.
Parental vaccine safety concerns in 2009.
(2010)

OBJECTIVE
Vaccine safety concerns can diminish parents' willingness to vaccinate their children. The objective of this study was to characterize the current prevalence of parental vaccine refusal and specific vaccine safety concerns and to determine whether such concerns were more common in specific population groups.


METHODS
In January 2009, as part of a larger study of parents and nonparents, 2521 online surveys were sent to a nationally representative sample of parents of children who were aged </=17 years. The main outcome measures were parental opinions on vaccine safety and whether the parent had ever refused a vaccine that a doctor recommended for his or her child.


RESULTS
The response rate was 62%. Most parents agreed that vaccines protect their child(ren) from diseases; however, more than half of the respondents also expressed concerns regarding serious adverse effects. Overall, 11.5% of the parents had refused at least 1 recommended vaccine. Women were more likely to be concerned about serious adverse effects, to believe that some vaccines cause autism, and to have ever refused a vaccine for their child(ren). Hispanic parents were more likely than white or black parents to report that they generally follow their doctor's recommendations about vaccines for their children and less likely to have ever refused a vaccine. Hispanic parents were also more likely to be concerned about serious adverse effects of vaccines and to believe that some vaccines cause autism.


CONCLUSIONS
Although parents overwhelmingly share the belief that vaccines are a good way to protect their children from disease, these same parents express concerns regarding the potential adverse effects and especially seem to question the safety of newer vaccines. Although information is available to address many vaccine safety concerns, such information is not reaching many parents in an effective or convincing manner.
Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory
(2000)

In 1998 and again in 1999, the Lincoln Laboratory of MIT conducted a comparative evaluation of intrusion detection systems (IDSs) developed under DARPA funding. While this evaluation represents a significant and monumental undertaking, there are a number of issues associated with its design and execution that remain unsettled. Some methodologies used in the evaluation are questionable and may have biased its results. One problem is that the evaluators have published relatively little concerning some of the more critical aspects of their work, such as validation of their test data. The appropriateness of the evaluation techniques used needs further investigation. The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing. Some of the problems that the article points out might well be resolved if the evaluators were to publish a detailed description of their procedures and the rationale that led to their adoption, but other problems would clearly remain./par>
Aberrant Behavior Detection in Time Series for Network Monitoring
(2000)

The open-source software RRDtool and Cricket provide a solution to the problem of collecting, storing, and visualizing service network time series data for the real-time monitoring task. However, simultaneously monitoring all service network time series of interest is an impossible task even for the accomplished network technician. The solution is to integrate a mathematical model for automatic aberrant behavior detection in time series into the monitoring software. While there are many such models one might choose, the primary goal should be a model compatible with real-time monitoring. At WebTV, the solution was to integrate a model based on exponential smoothing and Holt-Winters forecasting into the Cricket/RRDtool architecture. While perhaps not optimal, this solution is flexible, efficient, and effective as a tool for automatic aberrant behavior detection.
Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters
(2005)

Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously “unseen” attacks. In this paper, we present a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection. We evaluated our methods using the 1999 KDD Cup data set. Our evaluation shows that the accuracy of our approach is close to that of existing techniques reported in the literature, and has several advantages in terms of computational complexity.
Data Mining Approaches for Intrusion Detection
(1998)

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the s ndmailsystem call data and the network tcpdumpdata, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intraand interaudit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.
Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures
(2017)

Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.
Measurement of preoperative and postoperative nasal tip projection and rotation.
(2006)

OBJECTIVE
To measure the effect of columellar struts and cephalic trim on tip projection and tip rotation using digitized photographs.


METHODS
Using photographs of 62 patients who underwent external rhinoplasty, we retrospectively analyzed nasal tip projection (the Goode method) and rotation (nasolabial angle) before and after surgery. A cartilaginous strut was used in 36 patients, whereas 26 patients did not receive a strut. Patients were categorized into 4 subgroups, depending on the placement of a strut (placement, strut+ vs nonplacement, strut-) and the removal of the cephalic margin (removal, cephalic+ vs nonremoval, cephalic-) of the lateral crus: strut-/cephalic-, n = 17; strut+/cephalic-, n = 23; strut-/cephalic+, n = 9; strut+/cephalic+, n = 12.


RESULTS
Nasal tip projection, measured with the Goode method, increased from 0.58 to 0.60 (P = .02) in the strut+ group; in the strut- group, nasal tip projection did not change significantly. Nasolabial angle increased from 93.96 degrees to 100.92 degrees in the strut+/cephalic- group and from 88.30 degrees to 95.06 degrees in the strut+/cephalic+ group. Removal of the cephalic margin alone (strut-/cephalic+) hardly affected tip rotation (P = .05).


CONCLUSIONS
The external rhinoplasty approach did not lead to a decrease in nasal tip projection. A cartilaginous strut slightly increased nasal tip projection and also increased nasal tip rotation. This effect was accentuated by the removal of the cephalic margin of the lateral crus.
Labeled Faces in the Wild: A Survey
(2016)

In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this benchmark in some respect. A remarkably wide variety of innovative methods have been developed to overcome the challenges presented in this database. As performance on some aspects of the benchmark approaches 100% accuracy, it seems appropriate to review this progress, derive what general principles we can from these works, and identify key future challenges in face recognition. In this survey, we review the contributions to LFW for which the authors have provided results to the curators (results found on the LFW results web page). We also review the cross cutting topic of alignment and how it is used in various methods. We end with a brief discussion of recent databases designed to challenge the next generation of face recognition algorithms. Erik Learned-Miller University of Massachusetts, Amherst, Massachusetts, e-mail: elm@cs.umass.edu Gary B. Huang Howard Hughes Medical Institute, Janelia Research Campus, e-mail: gbhuang@cs.umass.edu Aruni RoyChowdhury University of Massachusetts, Amherst, Massachusetts, e-mail: aruni@cs.umass.edu Haoxiang Li Stevens Institute of Technology, Hoboken, New Jersey, e-mail: hli18@stevens.edu Gang Hua Stevens Institute of Technology, Hoboken, New Jersey, e-mail: ganghua@gmail.com
Open production: scientific foundation for co-creative product realization
(2011)

Globalization and the use of technology call for an adaptation of value creation strategies. As the potential for rationalization and achieving flexibility within companies is to the greatest possible extent exhausted, approaches to the corporate reorganization of value creation are becoming increasingly important. In this process, the spread and further development of information and communication technology often provide the basis for a reorganization of cross-company value nets and lead to a redistribution of roles and tasks between the actors involved in value creation. While cooperative, decentralized and self-organizing value creation processes are in fact being promoted, the associated potential for development and production engineering is being underestimated and hence not implemented sufficiently. This contribution will introduce a value creation taxonomy and then, using its notion and structure, describe the emerging transformations in value creation on the basis of case studies. Finally an adequate framework for analysing and configuring value creation will be presented.
Partisan Media Exposure and Attitudes Toward the Opposition
(2013)

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Steerable interfaces for pervasive computing spaces
(2003)

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.
Computational Intelligence Models for Insurance Fraud Detection : A Review of a Decade of Research
(2014)

This paper presents a review of the literature on the application of data mining techniques for the detection of insurance fraud. Academic literature were analyzed and classified into three types of insurance fraud (automobile insurance, crop insurance and healthcare insurance) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that automobile insurance fraud detection have also attracted a great deal of attention in recent years. The main data mining techniques used for insurance fraud detection are logistic models, Decision tree, the Naïve Bayes, and support vector machine.